We learn to identify TPs and the graph \(\mathcal {G}\)  that serves as
input to our trailer generation algorithm in tandem. We hypothesize
that the training signal provided by TP labels (i.e., the narrative
structure) also encourages exploring meaningful semantic connections
between shots via the graph that is learnt
[1]}. Each shot \(i\)  is represented by \(h_i\) , a
nonlinear combination of textual, visual, and acoustic modalities
\(h_i = f([t_i^{\prime }; v_i^{\prime }; a_i^{\prime }])\), where \(f(\cdot )\)  is a
fully-connected layer followed by the ReLU
non-linearity. We compute textual representation \(t_i^{\prime }\)  via the
bi-directional attention flow [2]}, [3]}
between subtitles \(t_i\)  and two other modalities, namely audio \(a_i\) 
and video frames \(v_i\) :
\(S_{t_i, a_i} = a_i^{T}t_i, \quad S_{t_i, v_i} = v_i^{T}t_i \\a_{i,att} = \operatorname{r}(S_{t_i,a_i})a_i, \quad v_{i,att} = \operatorname{r}(S_{t_i,v_i})v_i \\t_i^{\prime } = a_{i,att} + v_{i,att} + t_i\) 
